Comparison of Efficient Global Optimization and Output Space Mapping on the Biobjective Optimization of a Safety Isolating Transformer

Berbecea, Alexandru; Ben-Ayed, R.; Gillon, F.; Brisset, S.; Brisset, Pascal

doi:10.1109/tmag.2011.2174145

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…Facilitating EM‐driven design procedures has been the subject of intense research over the last two decades or so. Among the various techniques developed, some notable examples include adjoint sensitivities and their applications for accelerating gradient search procedures, 10 surrogate‐assisted frameworks involving both approximation 11‐13 and physics‐based models 14‐16 but also machine learning methods, 17 typically applied for global optimization (eg, EGO‐type of methods 18 ).…”

Section: Introductionmentioning

confidence: 99%

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Kriging metamodels and design re‐utilization for fast parameter tuning of antenna structures

Koziel

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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The paper addresses the problem of computationally efficient electromagnetic (EM)-driven design closure of antenna structures. The foundations of the presented approach are fast kriging interpolation metamodels, utilized for two purposes: (a) producing a good starting point for further parameter tuning, and (b) yielding a reasonable Jacobian matrix estimate to jump-start the optimization procedure. The models are rendered using available designs, for example, obtained from the previous design work with the same antenna structure. The low cost of design closure is ensured by employing Broydenbased trust-region gradient search along with the aforementioned Jacobian initialization. Our methodology is demonstrated using two planar antennas, a dual-band uniplanar dipole and a quasi-Yagi with a parabolic reflector, both optimized within wide ranges of operating conditions (center frequencies, the dielectric permittivity of the antenna substrate). The redesign process requires only a handful of EM analyses of the respective structure. The presented framework can be viewed as a convenient algorithmic tool that capitalizes on the existing information on the structure at hand to enable warm-start parameter tuning. K E Y W O R D S antenna design, design closure, EM simulation, kriging metamodels, parameter tuning 1 | INTRODUCTION Widespread utilization of electromagnetic (EM) simulation tools revolutionized the development procedures of modern antennas. As a matter of fact, EM-driven design is a practical necessity in a growing number of cases where analytical models are no longer adequate. It is especially pertinent to structures featuring nonnegligible mutual coupling effects 1 and feed radiation, 2 compact antennas, 3 wearable antennas at the presence of human body, 4 or situations where environmental components (connectors, housing) affect the antenna operation. 5 Perhaps the most common usage of EM tools is parameter tuning, 6,7 which is a computationally expensive endeavor. Its complexity may be aggravated by factors such as the need for global search, operating in highly-dimensional parameter spaces, or the need for handling multiple performance figures and constraints. 8,9 Convenience, CPU cost and availability of the algorithms (eg, implemented in commercial software packages) typically suggest the utilization of local optimization procedures, but these require reasonably good initial designs, which are not always at hand. Facilitating EM-driven design procedures has been the subject of intense research over the last two decades or so. Among the various techniques developed, some notable examples include adjoint sensitivities and their applications

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Section: Introductionmentioning

confidence: 99%

“…for accelerating gradient search procedures, 10 surrogate-assisted frameworks involving both approximation [11][12][13] and physics-based models [14][15][16] but also machine learning methods, 17 typically applied for global optimization (eg, EGO-type of methods 18 ).…”

mentioning

confidence: 99%

Kriging metamodels and design re‐utilization for fast parameter tuning of antenna structures

Koziel

Pietrenko‐Dabrowska

2020

Int J Numerical Modelling

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“…Another approach is algorithmic acceleration of local EM‐based optimisation procedures through sparse sensitivity updates [17, 18]. In the context of global optimisation, data‐driven models combined with sequential sampling strategies are used as well [19].…”

Section: Introductionmentioning

confidence: 99%

Low‐cost data‐driven modelling of microwave components using domain confinement and PCA‐based dimensionality reduction

Kozieł

Pietrenko‐Dabrowska

2020

IET Microwaves, Antennas & Propagation

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Fast data-driven surrogate models can be employed as replacements of computationally demanding full-wave electromagnetic simulations to facilitate the microwave design procedures. Unfortunately, practical application of surrogate modelling is often hindered by the curse of dimensionality and/or considerable nonlinearity of the component characteristics. This paper proposes a simple yet reliable approach to cost-efficient modelling of miniaturized microwave components which adopts two fundamental mechanisms to improve the computational efficiency of setting up the surrogate. Firstly, the model domain is confined-using a set of pre-optimized reference designs-to the region of the parameter space containing high-quality designs with respect to the relevant performance figures. Secondly, the domain is spanned by the selected principal components of the reference set for dimensionality reduction. Consequently, the surrogate model, covering wide ranges of the device parameters and operating conditions, can be established using a fraction of training data samples required by conventional approaches, without compromising its predictive power. The proposed technique is illustrated using two miniaturized microstrip components: a rat-race coupler (RRC) and an impedance matching transformer. The following accuracies of the PCA-based surrogates have been obtained: 0.9% for RRC and 2.1% for the transformer (for 800 training data samples).

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“…Yet, these models barely consider phenomena like acoustic vibrations or ripple torque (RT) as they are hard to model analytically. These phenomena can, however, be very well handled with finite-element (FE) analysis and some techniques, such as output space mapping [3], were developed to handle multigranular optimization.…”

Section: Introductionmentioning

confidence: 99%

Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

2015

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This paper proposes a method to the model constraints from different models to run an optimization over models with different granularities. Through machine learning, the proposed method has proven to be able to accurately map the constraints and minimize the number of call to the model. It handles both continuous and discrete variables and mixes design rules to statistic approach to create a surrogate of the model. Index Terms-Constraint modeling, finite-element (FE) model, machine learning, optimal design, random forest.

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Comparison of Efficient Global Optimization and Output Space Mapping on the Biobjective Optimization of a Safety Isolating Transformer

Cited by 9 publications

References 9 publications

Kriging metamodels and design re‐utilization for fast parameter tuning of antenna structures

Kriging metamodels and design re‐utilization for fast parameter tuning of antenna structures

Low‐cost data‐driven modelling of microwave components using domain confinement and PCA‐based dimensionality reduction

Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

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